1a) We will read the following book.
1b) Also see the review article .
1c) Also see
interesting projects from Stanford.
2) We will read a few related papers. Understand how Deep Learning may
be related to other topics in Data Sciences... Particular Emphasis:
AdTech (DMP and Attribution), FinTech (compare to Kensho, Palantir,
Sentient), Verifier-Recommenders (Cyber Security, Privacy, AI to
obfuscate AI), Caner, Linguistics, ...
3) Each participant will implement a deep learning application with
open source code (e.g., caffe or alexnet). In preparation for this we
will read the
4) Jointly with MIT, Cornell and UW, we have submitted a grant proposal to
NSF to understand interconnections among Manifold Learning, Deep
Learning and PH-Learning (under review).
5) For cancer applications, we have been funded by NCI to start
a center (focus on
causality & topology). Also we are organizing a summer school.
Fequently Asked Questions (FAQs)
Q1. Can I attend this class?
A1. Yes, but... only if you have been invited.
Q2. Can I get a grade/credit?
A2. No, this is an informal class. But we will write paper(s), which
will show how DEEP-ly we understand these topics. We can create some
programming projects, which can be evaluated and displayed on a
leader-board. We can also take up some problems in Kaggle and use
their evaluations to demonstrate depth of our understanding.
Any other ideas?